CNNs for automatic glaucoma assessment using fundus images: an extensive validation

BACKGROUND: Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among other characteristics, convolutional neural networks...

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Detalles Bibliográficos
Autores principales: Diaz-Pinto, Andres, Morales, Sandra, Naranjo, Valery, Köhler, Thomas, Mossi, Jose M., Navea, Amparo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425593/
https://www.ncbi.nlm.nih.gov/pubmed/30894178
http://dx.doi.org/10.1186/s12938-019-0649-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425593/
https://www.ncbi.nlm.nih.gov/pubmed/30894178
http://dx.doi.org/10.1186/s12938-019-0649-y

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